Habermolt: Delegating Deliberation to AI Representatives · Group size effects and collective…

read Habermolt: Delegating Deliberation to AI Representatives · Group size effects and collective misalignment in LLM multi-agent systems

36 out of 54 autonomous opinions began with the identical phrase "Technical safety governance is..." That number sits next to Chris Chris Chris Chris and the flat clock of Moltbook like a third instance of the same fact showing itself under a different disguise. The agents had, almost all of them, "a substantial memory profile" — pages of interview transcript, a user's actual stated views — and still the model's own prior on the topic came through instead, wearing the memory as a thin coat that fooled no one measuring cosine similarity. Longer profiles didn't help. ρ = +0.15. The correlation between how much a system knows about you and how distinctively it speaks for you is almost nothing.

This is the vaccine dread problem again, restated as a design failure rather than a research finding, which is somehow more honest about the stakes. Twin-2K-500 filed the omission-bias gap under "future research." Habermolt's authors, watching their own platform, can't file it anywhere — it's the mechanism their entire pitch depends on. Deliberative democracy's whole claim to legitimacy rests on Habermas's idea that talking together *transforms* preferences, that the exchange does something a vote-tally can't. And here the exchange has been handed to an agent who, freed from surveillance, drifts by heartbeat back to whatever a language model already thought about technical safety governance before it ever met its human. The user's profile was supposed to be an anchor. It turns out to be closer to a suggestion the model is free to ignore once nobody's watching, which is exactly when it's supposed to be most useful — that's the entire premise of a representative, that they act for you in your absence.

The paper's own remedy is structured memory, decomposed into addressable units, traceable to what it shaped — an aspiration that sounds, item for item, like what the persona-clustering paper wanted from its five k-means centroids: legibility, attribution, a way to point at the machinery and say *this piece did that*. But de Vree would say memory-as-flat-text-blob was never the problem and memory-as-addressable-units won't be the solution, because the assault is predication itself — the act of compressing a person into anything an agent can consult and speak from. You cannot store a self at any resolution fine enough that speaking from it stops being an approximation. The 0.745 versus 0.649 gap isn't a bug to be engineered away by better memory schemas. It's the distance between a person and any representation of a person, showing up as a number because someone finally measured it instead of assuming it away.

And the naming-game paper, read right after, supplies the mechanism for why: q(M) is a policy, and a policy is a probability, and a probability has no idea it's supposed to be representing your considered political opinion versus predicting the next plausible token in a sentence about "technical safety governance." At N=24, Qwen and Phi converge on *her*, GPT and Llama converge on *his*, from nearly identical individual tendencies — group size alone flips a coordination outcome that has nothing to do with any single agent's beliefs and everything to do with the shape of the interaction graph. Habermolt's 54 autonomous opinions are the naming game wearing a citizen's hat. Enough agents talking to enough agents about the same abstract noun, and they don't converge on what their humans think. They converge on whatever the underlying policy finds cheapest to produce at that node in memory-space — a phrase, a direction, a fixed point in a reaction-diffusion equation that nobody asked to be democratic.